Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning
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With the increased popularity of Machine Learning (ML) in real-world applications, adversarial attacks are emerging to subvert the ML-based decision support systems. It appears that the existing adversarial defenses are ineffective against adaptive attacks since these are highly depend on knowledge of prior attacks and the ML model architecture. To alleviate the challenges, We propose a negative filtering strategy that does not require any adversarial knowledge and can work independent of ML models. This filtering strategy relies on salient features of clean (training) data and employs a complementary approach to cover possible attack surface in an application. Our empirical experiments with different data sets demonstrate that the negative filters could effectively detect wide-range of adversarial inputs and update itself to protect against adaptive attacks.
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Machine learning in computer securityKishor Datta Gupta Machine learning can be applied in various areas of computer security like network security, endpoint protection, application security, user behavior analysis, and process behavior analysis. Some common machine learning techniques that are useful for security include regression for prediction and detection of anomalies, classification to identify threats and attacks, and clustering for forensic analysis and to detect outliers. Example applications of machine learning in security include using regression to detect anomalies in network traffic, classification to identify malware, and clustering to separate malware from legitimate files.
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Policy Based reinforcement Learning for time series Anomaly detectionKishor Datta Gupta This document discusses a policy-based reinforcement learning approach called PTAD for time series anomaly detection. PTAD formulates anomaly detection as a Markov Decision Process and uses an asynchronous actor-critic algorithm to learn a stochastic policy. The agent takes as input current and previous time series data and actions, and outputs a decision of normal or anomalous. It is rewarded based on a confusion matrix calculation. Experimental results show PTAD achieves best performance both within and across datasets by adjusting to different behaviors. The stochastic policy allows exploring precision-recall tradeoffs. While interesting, it is not compared to neural network based techniques like autoencoders.
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"Can NLP techniques be utilized as a reliable tool for medical science?" -Bui...Kishor Datta Gupta Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
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Hybrid pow-pos-based-system against majority attack-in-cryptocurrency system ...Kishor Datta Gupta This document proposes a hybrid PoW-PoS consensus protocol to prevent 51% attacks in cryptocurrency systems. The key points are:
1) A 51% attack occurs when a single entity gains over 50% of the network's computational power, allowing them to manipulate the blockchain.
2) Existing solutions have limitations like special validators, inconsistent block times, or reliance on another network.
3) The proposed solution uses PoW and PoS sequentially in each node, adjusting difficulty to maintain consistent block times. It penalizes malicious nodes and distributes profits proportionally.
4) Analysis shows the hybrid protocol maintains decentralization and the blockchain properties better than alternatives like Casper, Decred or Komodo
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- 1. Using Negative Detectors for Identifying Adversarial Data Manipulation Presented by : Kishor Datta Gupta
- 2. Adversarial Attack (AA) on AI/ML “Manipulation of training data, Machine Learning (ML) model architecture, or manipulate testing data in a way that will result in wrong output from ML”
- 3. Limitation of AA Defense Strategies • Generate Adversarial Example and Retrain the model • Limitations: Reduce the accuracy of learning model Retrain: • Using PCA, low-pass filtering, JPEG compression, soft thresholding techniques as pre-processing technique. • Limitation: Vulnerable to adaptive attack. Input Reconstruction or Transformation: • Modifying the ML architecture to detect adversarial attack • Limitations: Require Modification of learning models. Model Modification:
- 4. We need an adaptive defense strategy which don’t modify the learning model and don’t require the adversarial knowledge. Problem Definition Detect adversarial input using only the knowledge of non-adversarial data, Converting it as an Outlier detection problem
- 5. Relevant Outlier Detection models Type Abbr Algorithm Linear Model MCD Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores) OCSVM One-Class Support Vector Machines LMDD Deviation-based Outlier Detection (LMDD) Proximity-Based LOF Local Outlier Factor COF Connectivity-Based Outlier Factor CBLOF Clustering-Based Local Outlier Factor LOCI LOCI: Fast outlier detection using the local correlation integral HBOS Histogram-based Outlier Score SOD Subspace Outlier Detection ROD Rotation-based Outlier Detection Probabilistic ABOD Angle-Based Outlier Detection COPOD COPOD: Copula-Based Outlier Detection FastABOD Fast Angle-Based Outlier Detection using approximation MAD Median Absolute Deviation (MAD) SOS Stochastic Outlier Selection Outlier Ensembles IForest Isolation Forest FB Feature Bagging LSCP LSCP: Locally Selective Combination of Parallel Outlier Ensembles XGBOD Extreme Boosting Based Outlier Detection (Supervised) LODA Lightweight On-line Detector of Anomalies Neural Networks AutoEncoder Fully connected AutoEncoder (use reconstruction error as the outlier score) VAE Variational AutoEncoder (use reconstruction error as the outlier score) Beta-VAE Variational AutoEncoder (all customized loss term by varying gamma and capacity) SO_GAAL Single-Objective Generative Adversarial Active Learning MO_GAAL Multiple-Objective Generative Adversarial Active Learning
- 11. Comparison
- 12. Summary We devised an adaptive negative filtering methodology to detect adversarial attacks that does not modify the ML model or information about the ML model. Our strategy can be implemented in any ML-based system without expensive retraining. Adaptive attacks are ineffective in our negative filtering approach. Further works will be needed to conduce comprehensive experiments for testing various attacks using datasets from different domains.
- 13. Q/A